multi-label classification setting:将多标签分类任务视为多个二分类任务,有K个类别,模型输出logitzk然后输入到sigmoid函数,对应label为yk,total loss为各个类别binary loss(BCE)之和 img binary loss(BCE):对于单个类别,其binary loss常见形式为 img Focal Loss 其中p=σ(z)、γ是focusing parameter 当γ=0时,...
ASL的核心在于其创新策略。首先，它区别对待简单负样本（easy negatives），通过动态的soft-threshold和hard-threshold进行调整，使得模型更专注于那些难以区分的正标签。其次，ASL结合了Binary Cross-Entropy (BCE)和Focal Loss的优点，引入了asymmetric focusing，以优化对正负样本的关注点。ASL的定义包含对概率...
在做multi-label的问题时,常把样本的label转为0-1的vector。长尾分布一般都有很多个类别,但是一个样本的label可能只有几个类别。假设总共有K类,对于每个样本,label(K维0-1的vector)中的0的数量多于1的数量。NT-BCE loss为: 其中,λ 为影响loss梯度的scale factor,。 vi 为class-specific bias,对不同的class...
Classification General Classification Image Classification Multi-Label Classification object-detection Object Detection Datasets Edit MS COCO NUS-WIDE PASCAL VOC 2007 OpenImages-v6 Results from the Paper Edit Ranked #4 on Multi-Label Classification on NUS-WIDE Get a GitHub badge Task...
focal loss for multi-class classification 转自:https://blog.csdn.net/Umi_you/article/details/80982190 Focal loss 出自何恺明团队Focal Loss for Dense Object Detection一文,用于解决分类问题中数据类别不平衡以及判别难易程度差别的问题。文章中因用于目标检测区分前景和背景的二分类问题,公式以二分类问题为例。
Besides, compared with single-label classification, the labels of multi-label classification may have some correlations such as co-occurrence or conditional probability relationship. So we also propose a special regularization term for this model, which can help to exploit label correlations by using...
When we use Flair for Multi-Label Text Classification, we use the BCELoss. This works great but suffers badly in cases of class-imbalances in the data. An alternative for the same could be FocalLoss defined an release by Facebook. Focal loss is a Cross-Entropy Loss that weighs the ...
http://t.cn/A6bsfELp Asymmetric Loss For Multi-Label Classification 对highly unbalanced classification肉眼可见的好用 不光是Multi-Label, single label也可以用[思考]
we reach state-of-the-art results on multiple popular multi-label datasets: MS-COCO, Pascal-VOC, NUS-WIDE and Open Images. We also demonstrate ASL applicability for other tasks, such as single-label classification and object detection. ASL is effective, easy to implement, and does not increas...
Title:《Asymmetric Loss For Multi-Label Classification》Author: DAMO Academy, Alibaba GroupICCV 2021, code Highlight 本文解决的问题是,multi-label的正负样本比例严重不均衡,以及标签错误标注的问题。 提出了一个可以分别对正负样本做不同加权的损失函数。 Methods ASL={L+=(1−p)γ+log(p)L−=(pm...